EFFICIENCY OF PRESCRIPTION DRUG USE IN THE MEDICARE POPULATION

项目总结（见说明）：处方药在2010年占医疗保险支出的11%，但预计到2020年将占该预算的20%。这个子项目的目的是促进我们对处方药在医疗保险受益人中的使用和效率的理解。首先，我们将美国医疗保险人口在地区或医生-医院网络（PHN）水平上的药物使用分为四类，这取决于效率或有效性-即每美元花费的价值。从第一类非常有效的药物到第四类可能对健康造成不利影响的药物。在本分析及后续分析中，我们将考虑可能影响药物使用效率的因素，如护理碎片化、学术隶属关系、卫生信息技术采用、市场结构，以及对一些医生来说，美国内科医学委员会考试的委员会分数。这些度量也可以跨其他子项目分析使用，例如子项目1中的phn和子项目4中的区域。其次，我们关注特定人群的药物治疗：心肌梗死幸存者、髋部骨折幸存者和慢性肺部疾病事件病例。这种关注使我们能够更容易地根据患者水平因素进行调整，并确定明确有效的治疗方法（例如，在药物洗脱支架后使用氯吡格雷）。在以后的几年里，数据将允许评估支付结构的影响，如共享储蓄和部分资本化对处方使用措施。第三，我们使用前一小节中开发的队列来衡量替代效应，即明智地购买高效药物可能对下游医疗保健成本产生积极影响的程度，而对于那些价值鲜为人知（甚至是负值）的药物则相反。为了调整潜在风险领域之间的差异，我们将利用项目5的风险调整措施。最后，我们描述了不同人群中处方药使用的差异。早期工作表明，与总体人口相比，医疗保险亚组（如黑人、老年人、穷人）在使用某些有效药物方面存在实质性差异。此外，我们打算研究65岁以下的医疗保险人群（主要是那些接受社会保障残疾保险的人），他们对精神疾病的药物治疗可能很常见。

PROJECT SUMMARY (See instructions): Prescription drugs accounted for 11% of Medicare spending in 2010, but are expected to account for 20% of this budget by 2020. The purpose of this subproject is to advance our understanding of the use and efficiency of prescription drugs among Medicare beneficiaries. First, we characterize the use of pharmaceutical drugs in the U.S. Medicare population at the level of the region or physician-hospital network (PHN) into four categories depending on efficiency or effectiveness - that is, value per dollar spent. These range from the first category, highly effective, to the fourth category, drugs which could lead to adverse effects on health. In this and subsequent analyses, we will consider factors that may affect the efficiency of pharmaceutical use, such as care fragmentation, academic affiliation, health information technology adoption, market structure, and for some physicians, board scores from the American Board of Internal Medicine examinations. These measures can also be used across other subproject analyses, for example for PHNs in subproject 1 and regions in subproject 4. Second, we focus on pharmaceutical treatments for specific cohorts: myocardial infarction survivors, hip fracture survivors, and incident cases of chronic pulmonary disease. This focus allows us to adjust more easily for patient level factors and identify clearly effective treatments (e.g., clopidogrel following a drug-eluting stent). In later years, data will permit assessment of the impact of payment structure such as shared savings and partial capitation on prescription use measures. Third, we use the cohorts developed in the previous subsection to measure substitution effects, the extent to which spending wisely on highly effective pharmaceuticals may have positive effects on downstream healthcare costs, and conversely for those drugs with little known value (or even negative value). To adjust for differences across areas in underlying risk, we will draw on Project 5's risk adjustment measures. Finally, we describe differences in prescription drug use in population subgroups. Early work shows substantial differences in use of some effective medications among Medicare subgroups (e.g. Black, older, poor) compared to the population overall. In addition, we intend to study the under-65 Medicare population (largely those receiving Social Security Disability Insurance) where pharmaceutical treatment for mental illness is likely to be common.